An integrated approach for investigating the biomolecular responses to Ahr ligands necessitates the[unreadable] application of modern genomic and proteomic tools. By utilizing these tools, the individual projects will be[unreadable] generating significant amounts of data that must be managed, stored, analyzed, and mined in order to derive[unreadable] knowledge from the mass of information. The Biomedical Informatics Core (BIG) was formed to perform[unreadable] these functions. The primary mission of the BIC is to enable the biomedical investigators to derive optimal[unreadable] use of genomic and proteomic data in the most efficient manner possible. To obtain this goal, the general[unreadable] philosophy of the BIC is that responsibility for data interpretation should reside with the individual[unreadable] investigators since they are the experts in the biological systems in which they are studying. Our[unreadable] responsibility as a core is to provide consistent preliminary data analysis, software tools, data management[unreadable] infrastructure, and training necessary for their success. This mission will be implemented through the[unreadable] following specific aims: (1) develop the infrastructure necessary to manage and store microarray and[unreadable] tandem affinity purification (TAP) data; (2) perform initial microarray data analysis for biomedical[unreadable] investigators and provide commercial and custom software tools for the visualization and analysis of[unreadable] microarray and TAP data; and (3) provide training and support to all biomedical investigators on the[unreadable] application and utilization of the software tools in relation to their specific analysis needs. For the large,[unreadable] complex datasets present in genomic and proteomic studies, the visual and intuitive capabilities of individual[unreadable] investigators must be coupled with computational analysis in order to recognize important patterns within the[unreadable] data. Establishing the BIC will insure a central repository will exist for all gene expression and TAP data and[unreadable] will enable efficient data mining to occur within and across projects.